使用布尔数组的不同组合作为键的 pandas 分组结果 [英] pandas groupby result using different combinations of boolean array as keys

问题描述

我试图通过使用布尔数组作为关键字来理解组，这是测试代码，

I tried to understand the groupby using boolean array as key, here is the test code,

a = pd.DataFrame([[True,False,False],[False,True,False]], columns=['A','B','C'])
print(a)

     A      B      C
0   True  False  False
1  False   True  False

然后我尝试了布尔数组的不同组合，看来groupby的结果都是一样的r

Then I tried different combination of boolean array, which seems that the groupby result are all the same r

b=a.groupby([False,False])
b.apply(pd.DataFrame)

       A     B       C
0   True    False   False
1   False   True    False

c=a.groupby([True,False])
c.apply(pd.DataFrame)

      A      B       C
0   True    False   False
1   False   True    False

d=a.groupby([False,True])
d.apply(pd.DataFrame)

     A       B       C
0   True    False   False
1   False   True    False

e=a.groupby([False,True])
e.apply(pd.DataFrame)

     A       B       C
0   True    False   False
1   False   True    False

推荐答案

让我们分解一下

.groubpy().apply(pd.DataFrame) 正如您在所有变体中使用的那样，从每个组中获取行并创建一个数据帧，它基本上返回 self，所以输出看起来是一样的，但熊猫到达那里的方式在每种情况下都不同

.groubpy().apply(pd.DataFrame) as you use in all variants takes the rows from each group and creates a dataframe, which basically returns self, so the output looks the same, but the way pandas gets there is different in every case

1. b=a.groupby([False,False]):两行属于同一个组(group_id False)，一起解析一次形成相同的df

1. b=a.groupby([False,False]): both rows belong to the same group (group_id False), and are parsed together once to form the same df

c=a.groupby([True,False]):有两组，每组一行.Apply 获取每个组并构建两个单独的 DataFrames(每组一个).然后连接并返回一个与原始相同的 df

c=a.groupby([True,False]): there are two groups with one row each. Apply takes each group and builds two separate DataFrames (one per group). Then concatenates and returs a df identical to original

d=a.groupby([False,True]):与#2 相同，但现在第一行属于组False.如果您聚合或应用了不同的函数(pandas.DataFrame 除外)，您会看到带有 True, False 的 df 作为索引(默认为 groupby 排序)，第 1 行将显示为第一行，因为它属于 True

d=a.groupby([False,True]): same as #2 but now the first row belongs to group False. If you aggregated or applied a different function (other than pandas.DataFrame) you would see the df with True, False as the index (groupby sorts by default) and row 1 would appear as the first row, because it belongs to group True

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